Characterization of pavement surface texture is important for pavement management applications. Surface texture can affect road characteristics and vehicle performance in the areas of tire wear, rolling resistance, tire/road friction, noise in vehicles, exterior road noise, discomfort and wear in vehicles (ISO 13473-1 1997). Pavement macro- and microtexture have significant impacts on skid resistance and generated noise.
Many of the pavement texture measurement devices reduce the data to a single attribute such as mean profile depth or hydraulic radius. Texture size, spacing, and distribution should also be considered. Therefore, advanced methods that characterize pavement texture in three dimensions are needed.
Photometric stereo technique is an example of a technique for characterizing texture of a surface in three dimensions. Specularity is an important consideration when using photometric stereo technique; therefore, different algorithms have been introduced to recover the shape of specular surfaces. Coleman and Jain [1], proposed a method to detect specularity component from four-source stereo technique by calculating four surface reflectance factors, one for each three-source combination. The deviation of the calculated reflectance factors is tested against a threshold value. If specularity exists, the combination of the photos that gives the smallest reflectance factor will be used to compute the surface normals. Ikeuchi [2] used a linear light source to study specular surfaces.
In the prior art according to Coleman and Jain a photometric stereo technique is applied for multiple light sources but which requires a complex calculation to determine if there is specularity in the images captured such that the resulting algorithm is slow and cumbersome, while unsatisfactorily overcoming errors due to specularity.